{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# \"Backtesting with your own custom strategy\"\n",
    "> \"Write your own buy and sell signals from custom indicators and built-in indicators\"\n",
    "\n",
    "- toc: true\n",
    "- branch: master\n",
    "- badges: true\n",
    "- comments: true\n",
    "- author: Benj Del Mundo\n",
    "- categories: [backtest, custom strategy]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Overview\n",
    "\n",
    "In this example, we will\n",
    "1. Create a new indicator outside fastquant (this could be anything from time-series methods to machine-learning-based methods)\n",
    "2. Combine our new indicator with some built-in indicators in our strategy\n",
    "3. Use multiple conditions on buy and sell signals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "# uncomment to install in colab\n",
    "# !pip3 install fastquant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastquant import backtest,get_stock_data\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[*********************100%***********************]  1 of 1 completed\n"
     ]
    }
   ],
   "source": [
    "df = get_stock_data(\"AAPL\",start_date='2019-01-01',end_date='2019-06-01')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>38.722500</td>\n",
       "      <td>39.712502</td>\n",
       "      <td>38.557499</td>\n",
       "      <td>39.480000</td>\n",
       "      <td>148158800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>35.994999</td>\n",
       "      <td>36.430000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>35.547501</td>\n",
       "      <td>365248800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>36.132500</td>\n",
       "      <td>37.137501</td>\n",
       "      <td>35.950001</td>\n",
       "      <td>37.064999</td>\n",
       "      <td>234428400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>37.174999</td>\n",
       "      <td>37.207500</td>\n",
       "      <td>36.474998</td>\n",
       "      <td>36.982498</td>\n",
       "      <td>219111200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>37.389999</td>\n",
       "      <td>37.955002</td>\n",
       "      <td>37.130001</td>\n",
       "      <td>37.687500</td>\n",
       "      <td>164101200</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close     volume\n",
       "dt                                                               \n",
       "2019-01-02  38.722500  39.712502  38.557499  39.480000  148158800\n",
       "2019-01-03  35.994999  36.430000  35.500000  35.547501  365248800\n",
       "2019-01-04  36.132500  37.137501  35.950001  37.064999  234428400\n",
       "2019-01-07  37.174999  37.207500  36.474998  36.982498  219111200\n",
       "2019-01-08  37.389999  37.955002  37.130001  37.687500  164101200"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create our own custom indicator. In this case, we'll use scipy implementation of Arnaud Legoux Moving Average (ALMA)\n",
    "\n",
    "> Arnaud Legoux Moving Average (ALMA) removes small price fluctuations and enhances the trend by applying a moving average twice, once from left to right, and once from right to left. At the end of this process the phase shift (price lag) commonly associated with moving averages is significantly reduced\n",
    "\n",
    "(https://www.interactivebrokers.com/en/software/tws/usersguidebook/technicalanalytics/arnaudlegoux.htm)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy.ndimage import convolve1d as conv\n",
    "\n",
    "def alma_indicator(data,window=9,offset=0.85,sigma=6):\n",
    "    m = int(offset * window-1)\n",
    "    s = window/sigma\n",
    "    dss = 2*s*s\n",
    "    wtds = np.exp(-(np.arange(window) - m)**2/dss)\n",
    "    return conv(data, weights=wtds/wtds.sum(),axis=0, mode='nearest')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4293094bb0>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "df[\"alma\"] = alma_indicator(df.close, window=9,offset=0.85,sigma=6)\n",
    "df[\"sma\"] = df.close.rolling(9).mean()\n",
    "\n",
    "\n",
    "df[[\"close\",\"alma\",\"sma\"]].plot(figsize=(30,10),title=\"Comparison of SMA(9) vs ALMA(9)\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>open</th>\n",
       "      <th>high</th>\n",
       "      <th>low</th>\n",
       "      <th>close</th>\n",
       "      <th>volume</th>\n",
       "      <th>alma</th>\n",
       "      <th>sma</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dt</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2019-01-02</th>\n",
       "      <td>38.722500</td>\n",
       "      <td>39.712502</td>\n",
       "      <td>38.557499</td>\n",
       "      <td>39.480000</td>\n",
       "      <td>148158800</td>\n",
       "      <td>39.309765</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-03</th>\n",
       "      <td>35.994999</td>\n",
       "      <td>36.430000</td>\n",
       "      <td>35.500000</td>\n",
       "      <td>35.547501</td>\n",
       "      <td>365248800</td>\n",
       "      <td>38.916997</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-04</th>\n",
       "      <td>36.132500</td>\n",
       "      <td>37.137501</td>\n",
       "      <td>35.950001</td>\n",
       "      <td>37.064999</td>\n",
       "      <td>234428400</td>\n",
       "      <td>38.217196</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-07</th>\n",
       "      <td>37.174999</td>\n",
       "      <td>37.207500</td>\n",
       "      <td>36.474998</td>\n",
       "      <td>36.982498</td>\n",
       "      <td>219111200</td>\n",
       "      <td>37.482952</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2019-01-08</th>\n",
       "      <td>37.389999</td>\n",
       "      <td>37.955002</td>\n",
       "      <td>37.130001</td>\n",
       "      <td>37.687500</td>\n",
       "      <td>164101200</td>\n",
       "      <td>37.115681</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 open       high        low      close     volume       alma  \\\n",
       "dt                                                                             \n",
       "2019-01-02  38.722500  39.712502  38.557499  39.480000  148158800  39.309765   \n",
       "2019-01-03  35.994999  36.430000  35.500000  35.547501  365248800  38.916997   \n",
       "2019-01-04  36.132500  37.137501  35.950001  37.064999  234428400  38.217196   \n",
       "2019-01-07  37.174999  37.207500  36.474998  36.982498  219111200  37.482952   \n",
       "2019-01-08  37.389999  37.955002  37.130001  37.687500  164101200  37.115681   \n",
       "\n",
       "            sma  \n",
       "dt               \n",
       "2019-01-02  NaN  \n",
       "2019-01-03  NaN  \n",
       "2019-01-04  NaN  \n",
       "2019-01-07  NaN  \n",
       "2019-01-08  NaN  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Implementing our custom strategy\n",
    "\n",
    "In this strategy we will have the following signals:\n",
    "\n",
    "Buy on:\n",
    "- Closing price is above ALMA\n",
    "- MACD crosses above the MACD signal line\n",
    "\n",
    "Sell on:\n",
    "- Closing price falls below ALMA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from fastquant import CustomStrategy, BaseStrategy\n",
    "from fastquant.indicators import MACD, CrossOver \n",
    "from fastquant.indicators.custom import CustomIndicator\n",
    "\n",
    "\n",
    "# Create a subclass of the BaseStrategy, We call this MAMAStrategy (MACD + ALMA)\n",
    "class MAMAStrategy(BaseStrategy):\n",
    "    \n",
    "    params = (\n",
    "        (\"alma_column\", \"alma\"),   # name for the ALMA column from the dataframe\n",
    "        (\"macd_fast_period\", 12),  # period for the MACD\n",
    "        (\"macd_slow_period\", 16),\n",
    "        (\"macd_signal_period\",9)\n",
    "    )\n",
    "\n",
    "    def __init__(self):\n",
    "        # Initialize global variables\n",
    "        super().__init__()\n",
    "        \n",
    "        # Setup MACD indicator parameters\n",
    "        self.macd_fast_period = self.params.macd_fast_period\n",
    "        self.macd_slow_period = self.params.macd_slow_period\n",
    "        self.macd_signal_period = self.params.macd_signal_period\n",
    "       \n",
    "        \n",
    "        # Setup MACD indicator, macd line and macd signal line, and macd signal line crossover\n",
    "        self.macd_ind = MACD(\n",
    "            period_me1=self.macd_fast_period, \n",
    "            period_me2=self.macd_slow_period, \n",
    "            period_signal=self.macd_signal_period\n",
    "        )\n",
    "        self.macd = self.macd_ind.macd\n",
    "        self.macd_signal = self.macd_ind.signal\n",
    "        \n",
    "        # Add signal line cross over\n",
    "        self.macd_signal_crossover = CrossOver(\n",
    "            self.macd_ind, self.macd_signal\n",
    "        )\n",
    "        \n",
    "        # Assign ALMA column from the dataframe\n",
    "        self.alma_column = self.params.alma_column\n",
    "        \n",
    "        # Set ALMA indicator from the alma column of data\n",
    "        self.alma = CustomIndicator(\n",
    "            self.data, custom_column=self.alma_column,\n",
    "        )\n",
    "        \n",
    "        # Plot the ALMA indicator along with the price instead of a separate plot\n",
    "        self.alma.plotinfo.subplot = False\n",
    "        self.alma.plotinfo.plotname = \"ALMA\"\n",
    "\n",
    "        print(\"===Strategy level arguments===\")\n",
    "        print(\"PARAMS: \", self.params)\n",
    "        \n",
    "\n",
    "    # Buy when the custom indicator is below the lower limit, and sell when it's above the upper limit\n",
    "    def buy_signal(self):\n",
    "        alma_buy =  self.dataclose[0] > self.alma[0]    # Close is above ALMA\n",
    "        macd_buy = self.macd_signal_crossover > 0       # MACD crosses signal line upward\n",
    "        \n",
    "        \n",
    "        return alma_buy and macd_buy \n",
    "    def sell_signal(self):\n",
    "        return self.alma[0] > self.dataclose[0]\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "===Global level arguments===\n",
      "init_cash : 100000\n",
      "buy_prop : 1\n",
      "sell_prop : 1\n",
      "commission : 0.0075\n",
      "stop_loss : None\n",
      "stop_trail : None\n",
      "===Strategy level arguments===\n",
      "PARAMS:  <backtrader.metabase.AutoInfoClass_LineRoot_LineMultiple_LineSeries_LineIterator_DataAccessor_StrategyBase_Strategy_BaseStrategy_MAMAStrategy object at 0x7f42935f0cd0>\n",
      "Final Portfolio Value: 93388.75828515051\n",
      "Final PnL: -6611.24\n",
      "Time used (seconds): 0.05437421798706055\n",
      "Optimal parameters: {'init_cash': 100000, 'buy_prop': 1, 'sell_prop': 1, 'commission': 0.0075, 'stop_loss': None, 'stop_trail': None, 'execution_type': 'close', 'channel': None, 'symbol': None, 'allow_short': False, 'short_max': 1.5, 'add_cash_amount': None, 'add_cash_freq': 'M', 'alma_column': 'alma', 'macd_fast_period': 12, 'macd_slow_period': 16, 'macd_signal_period': 9}\n",
      "Optimal metrics: {'rtot': -0.06839920896995008, 'ravg': -0.0006576847016341354, 'rnorm': -0.152730577686816, 'rnorm100': -15.2730577686816, 'len': 49, 'drawdown': 12.742882982202364, 'moneydown': 13638.337586126348, 'max': AutoOrderedDict([('len', 49), ('drawdown', 12.742882982202364), ('moneydown', 13638.337586126348)]), 'maxdrawdown': 12.742882982202364, 'maxdrawdownperiod': 49, 'sharperatio': None, 'pnl': -6611.24, 'final_value': 93388.75828515051}\n"
     ]
    },
    {
     "data": {
      "application/javascript": [
       "/* Put everything inside the global mpl namespace */\n",
       "window.mpl = {};\n",
       "\n",
       "\n",
       "mpl.get_websocket_type = function() {\n",
       "    if (typeof(WebSocket) !== 'undefined') {\n",
       "        return WebSocket;\n",
       "    } else if (typeof(MozWebSocket) !== 'undefined') {\n",
       "        return MozWebSocket;\n",
       "    } else {\n",
       "        alert('Your browser does not have WebSocket support. ' +\n",
       "              'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
       "              'Firefox 4 and 5 are also supported but you ' +\n",
       "              'have to enable WebSockets in about:config.');\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
       "    this.id = figure_id;\n",
       "\n",
       "    this.ws = websocket;\n",
       "\n",
       "    this.supports_binary = (this.ws.binaryType != undefined);\n",
       "\n",
       "    if (!this.supports_binary) {\n",
       "        var warnings = document.getElementById(\"mpl-warnings\");\n",
       "        if (warnings) {\n",
       "            warnings.style.display = 'block';\n",
       "            warnings.textContent = (\n",
       "                \"This browser does not support binary websocket messages. \" +\n",
       "                    \"Performance may be slow.\");\n",
       "        }\n",
       "    }\n",
       "\n",
       "    this.imageObj = new Image();\n",
       "\n",
       "    this.context = undefined;\n",
       "    this.message = undefined;\n",
       "    this.canvas = undefined;\n",
       "    this.rubberband_canvas = undefined;\n",
       "    this.rubberband_context = undefined;\n",
       "    this.format_dropdown = undefined;\n",
       "\n",
       "    this.image_mode = 'full';\n",
       "\n",
       "    this.root = $('<div/>');\n",
       "    this._root_extra_style(this.root)\n",
       "    this.root.attr('style', 'display: inline-block');\n",
       "\n",
       "    $(parent_element).append(this.root);\n",
       "\n",
       "    this._init_header(this);\n",
       "    this._init_canvas(this);\n",
       "    this._init_toolbar(this);\n",
       "\n",
       "    var fig = this;\n",
       "\n",
       "    this.waiting = false;\n",
       "\n",
       "    this.ws.onopen =  function () {\n",
       "            fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
       "            fig.send_message(\"send_image_mode\", {});\n",
       "            if (mpl.ratio != 1) {\n",
       "                fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
       "            }\n",
       "            fig.send_message(\"refresh\", {});\n",
       "        }\n",
       "\n",
       "    this.imageObj.onload = function() {\n",
       "            if (fig.image_mode == 'full') {\n",
       "                // Full images could contain transparency (where diff images\n",
       "                // almost always do), so we need to clear the canvas so that\n",
       "                // there is no ghosting.\n",
       "                fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
       "            }\n",
       "            fig.context.drawImage(fig.imageObj, 0, 0);\n",
       "        };\n",
       "\n",
       "    this.imageObj.onunload = function() {\n",
       "        fig.ws.close();\n",
       "    }\n",
       "\n",
       "    this.ws.onmessage = this._make_on_message_function(this);\n",
       "\n",
       "    this.ondownload = ondownload;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_header = function() {\n",
       "    var titlebar = $(\n",
       "        '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
       "        'ui-helper-clearfix\"/>');\n",
       "    var titletext = $(\n",
       "        '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
       "        'text-align: center; padding: 3px;\"/>');\n",
       "    titlebar.append(titletext)\n",
       "    this.root.append(titlebar);\n",
       "    this.header = titletext[0];\n",
       "}\n",
       "\n",
       "\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_canvas = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var canvas_div = $('<div/>');\n",
       "\n",
       "    canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
       "\n",
       "    function canvas_keyboard_event(event) {\n",
       "        return fig.key_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    canvas_div.keydown('key_press', canvas_keyboard_event);\n",
       "    canvas_div.keyup('key_release', canvas_keyboard_event);\n",
       "    this.canvas_div = canvas_div\n",
       "    this._canvas_extra_style(canvas_div)\n",
       "    this.root.append(canvas_div);\n",
       "\n",
       "    var canvas = $('<canvas/>');\n",
       "    canvas.addClass('mpl-canvas');\n",
       "    canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
       "\n",
       "    this.canvas = canvas[0];\n",
       "    this.context = canvas[0].getContext(\"2d\");\n",
       "\n",
       "    var backingStore = this.context.backingStorePixelRatio ||\n",
       "\tthis.context.webkitBackingStorePixelRatio ||\n",
       "\tthis.context.mozBackingStorePixelRatio ||\n",
       "\tthis.context.msBackingStorePixelRatio ||\n",
       "\tthis.context.oBackingStorePixelRatio ||\n",
       "\tthis.context.backingStorePixelRatio || 1;\n",
       "\n",
       "    mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
       "\n",
       "    var rubberband = $('<canvas/>');\n",
       "    rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
       "\n",
       "    var pass_mouse_events = true;\n",
       "\n",
       "    canvas_div.resizable({\n",
       "        start: function(event, ui) {\n",
       "            pass_mouse_events = false;\n",
       "        },\n",
       "        resize: function(event, ui) {\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "        stop: function(event, ui) {\n",
       "            pass_mouse_events = true;\n",
       "            fig.request_resize(ui.size.width, ui.size.height);\n",
       "        },\n",
       "    });\n",
       "\n",
       "    function mouse_event_fn(event) {\n",
       "        if (pass_mouse_events)\n",
       "            return fig.mouse_event(event, event['data']);\n",
       "    }\n",
       "\n",
       "    rubberband.mousedown('button_press', mouse_event_fn);\n",
       "    rubberband.mouseup('button_release', mouse_event_fn);\n",
       "    // Throttle sequential mouse events to 1 every 20ms.\n",
       "    rubberband.mousemove('motion_notify', mouse_event_fn);\n",
       "\n",
       "    rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
       "    rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
       "\n",
       "    canvas_div.on(\"wheel\", function (event) {\n",
       "        event = event.originalEvent;\n",
       "        event['data'] = 'scroll'\n",
       "        if (event.deltaY < 0) {\n",
       "            event.step = 1;\n",
       "        } else {\n",
       "            event.step = -1;\n",
       "        }\n",
       "        mouse_event_fn(event);\n",
       "    });\n",
       "\n",
       "    canvas_div.append(canvas);\n",
       "    canvas_div.append(rubberband);\n",
       "\n",
       "    this.rubberband = rubberband;\n",
       "    this.rubberband_canvas = rubberband[0];\n",
       "    this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
       "    this.rubberband_context.strokeStyle = \"#000000\";\n",
       "\n",
       "    this._resize_canvas = function(width, height) {\n",
       "        // Keep the size of the canvas, canvas container, and rubber band\n",
       "        // canvas in synch.\n",
       "        canvas_div.css('width', width)\n",
       "        canvas_div.css('height', height)\n",
       "\n",
       "        canvas.attr('width', width * mpl.ratio);\n",
       "        canvas.attr('height', height * mpl.ratio);\n",
       "        canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
       "\n",
       "        rubberband.attr('width', width);\n",
       "        rubberband.attr('height', height);\n",
       "    }\n",
       "\n",
       "    // Set the figure to an initial 600x600px, this will subsequently be updated\n",
       "    // upon first draw.\n",
       "    this._resize_canvas(600, 600);\n",
       "\n",
       "    // Disable right mouse context menu.\n",
       "    $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
       "        return false;\n",
       "    });\n",
       "\n",
       "    function set_focus () {\n",
       "        canvas.focus();\n",
       "        canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    window.setTimeout(set_focus, 100);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items) {\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) {\n",
       "            // put a spacer in here.\n",
       "            continue;\n",
       "        }\n",
       "        var button = $('<button/>');\n",
       "        button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
       "                        'ui-button-icon-only');\n",
       "        button.attr('role', 'button');\n",
       "        button.attr('aria-disabled', 'false');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "\n",
       "        var icon_img = $('<span/>');\n",
       "        icon_img.addClass('ui-button-icon-primary ui-icon');\n",
       "        icon_img.addClass(image);\n",
       "        icon_img.addClass('ui-corner-all');\n",
       "\n",
       "        var tooltip_span = $('<span/>');\n",
       "        tooltip_span.addClass('ui-button-text');\n",
       "        tooltip_span.html(tooltip);\n",
       "\n",
       "        button.append(icon_img);\n",
       "        button.append(tooltip_span);\n",
       "\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    var fmt_picker_span = $('<span/>');\n",
       "\n",
       "    var fmt_picker = $('<select/>');\n",
       "    fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
       "    fmt_picker_span.append(fmt_picker);\n",
       "    nav_element.append(fmt_picker_span);\n",
       "    this.format_dropdown = fmt_picker[0];\n",
       "\n",
       "    for (var ind in mpl.extensions) {\n",
       "        var fmt = mpl.extensions[ind];\n",
       "        var option = $(\n",
       "            '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
       "        fmt_picker.append(option);\n",
       "    }\n",
       "\n",
       "    // Add hover states to the ui-buttons\n",
       "    $( \".ui-button\" ).hover(\n",
       "        function() { $(this).addClass(\"ui-state-hover\");},\n",
       "        function() { $(this).removeClass(\"ui-state-hover\");}\n",
       "    );\n",
       "\n",
       "    var status_bar = $('<span class=\"mpl-message\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
       "    // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
       "    // which will in turn request a refresh of the image.\n",
       "    this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_message = function(type, properties) {\n",
       "    properties['type'] = type;\n",
       "    properties['figure_id'] = this.id;\n",
       "    this.ws.send(JSON.stringify(properties));\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.send_draw_message = function() {\n",
       "    if (!this.waiting) {\n",
       "        this.waiting = true;\n",
       "        this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
       "    }\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    var format_dropdown = fig.format_dropdown;\n",
       "    var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
       "    fig.ondownload(fig, format);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
       "    var size = msg['size'];\n",
       "    if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
       "        fig._resize_canvas(size[0], size[1]);\n",
       "        fig.send_message(\"refresh\", {});\n",
       "    };\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
       "    var x0 = msg['x0'] / mpl.ratio;\n",
       "    var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
       "    var x1 = msg['x1'] / mpl.ratio;\n",
       "    var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
       "    x0 = Math.floor(x0) + 0.5;\n",
       "    y0 = Math.floor(y0) + 0.5;\n",
       "    x1 = Math.floor(x1) + 0.5;\n",
       "    y1 = Math.floor(y1) + 0.5;\n",
       "    var min_x = Math.min(x0, x1);\n",
       "    var min_y = Math.min(y0, y1);\n",
       "    var width = Math.abs(x1 - x0);\n",
       "    var height = Math.abs(y1 - y0);\n",
       "\n",
       "    fig.rubberband_context.clearRect(\n",
       "        0, 0, fig.canvas.width / mpl.ratio, fig.canvas.height / mpl.ratio);\n",
       "\n",
       "    fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
       "    // Updates the figure title.\n",
       "    fig.header.textContent = msg['label'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
       "    var cursor = msg['cursor'];\n",
       "    switch(cursor)\n",
       "    {\n",
       "    case 0:\n",
       "        cursor = 'pointer';\n",
       "        break;\n",
       "    case 1:\n",
       "        cursor = 'default';\n",
       "        break;\n",
       "    case 2:\n",
       "        cursor = 'crosshair';\n",
       "        break;\n",
       "    case 3:\n",
       "        cursor = 'move';\n",
       "        break;\n",
       "    }\n",
       "    fig.rubberband_canvas.style.cursor = cursor;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
       "    fig.message.textContent = msg['message'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
       "    // Request the server to send over a new figure.\n",
       "    fig.send_draw_message();\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
       "    fig.image_mode = msg['mode'];\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Called whenever the canvas gets updated.\n",
       "    this.send_message(\"ack\", {});\n",
       "}\n",
       "\n",
       "// A function to construct a web socket function for onmessage handling.\n",
       "// Called in the figure constructor.\n",
       "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
       "    return function socket_on_message(evt) {\n",
       "        if (evt.data instanceof Blob) {\n",
       "            /* FIXME: We get \"Resource interpreted as Image but\n",
       "             * transferred with MIME type text/plain:\" errors on\n",
       "             * Chrome.  But how to set the MIME type?  It doesn't seem\n",
       "             * to be part of the websocket stream */\n",
       "            evt.data.type = \"image/png\";\n",
       "\n",
       "            /* Free the memory for the previous frames */\n",
       "            if (fig.imageObj.src) {\n",
       "                (window.URL || window.webkitURL).revokeObjectURL(\n",
       "                    fig.imageObj.src);\n",
       "            }\n",
       "\n",
       "            fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
       "                evt.data);\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "        else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
       "            fig.imageObj.src = evt.data;\n",
       "            fig.updated_canvas_event();\n",
       "            fig.waiting = false;\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        var msg = JSON.parse(evt.data);\n",
       "        var msg_type = msg['type'];\n",
       "\n",
       "        // Call the  \"handle_{type}\" callback, which takes\n",
       "        // the figure and JSON message as its only arguments.\n",
       "        try {\n",
       "            var callback = fig[\"handle_\" + msg_type];\n",
       "        } catch (e) {\n",
       "            console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
       "            return;\n",
       "        }\n",
       "\n",
       "        if (callback) {\n",
       "            try {\n",
       "                // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
       "                callback(fig, msg);\n",
       "            } catch (e) {\n",
       "                console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
       "            }\n",
       "        }\n",
       "    };\n",
       "}\n",
       "\n",
       "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
       "mpl.findpos = function(e) {\n",
       "    //this section is from http://www.quirksmode.org/js/events_properties.html\n",
       "    var targ;\n",
       "    if (!e)\n",
       "        e = window.event;\n",
       "    if (e.target)\n",
       "        targ = e.target;\n",
       "    else if (e.srcElement)\n",
       "        targ = e.srcElement;\n",
       "    if (targ.nodeType == 3) // defeat Safari bug\n",
       "        targ = targ.parentNode;\n",
       "\n",
       "    // jQuery normalizes the pageX and pageY\n",
       "    // pageX,Y are the mouse positions relative to the document\n",
       "    // offset() returns the position of the element relative to the document\n",
       "    var x = e.pageX - $(targ).offset().left;\n",
       "    var y = e.pageY - $(targ).offset().top;\n",
       "\n",
       "    return {\"x\": x, \"y\": y};\n",
       "};\n",
       "\n",
       "/*\n",
       " * return a copy of an object with only non-object keys\n",
       " * we need this to avoid circular references\n",
       " * http://stackoverflow.com/a/24161582/3208463\n",
       " */\n",
       "function simpleKeys (original) {\n",
       "  return Object.keys(original).reduce(function (obj, key) {\n",
       "    if (typeof original[key] !== 'object')\n",
       "        obj[key] = original[key]\n",
       "    return obj;\n",
       "  }, {});\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.mouse_event = function(event, name) {\n",
       "    var canvas_pos = mpl.findpos(event)\n",
       "\n",
       "    if (name === 'button_press')\n",
       "    {\n",
       "        this.canvas.focus();\n",
       "        this.canvas_div.focus();\n",
       "    }\n",
       "\n",
       "    var x = canvas_pos.x * mpl.ratio;\n",
       "    var y = canvas_pos.y * mpl.ratio;\n",
       "\n",
       "    this.send_message(name, {x: x, y: y, button: event.button,\n",
       "                             step: event.step,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "\n",
       "    /* This prevents the web browser from automatically changing to\n",
       "     * the text insertion cursor when the button is pressed.  We want\n",
       "     * to control all of the cursor setting manually through the\n",
       "     * 'cursor' event from matplotlib */\n",
       "    event.preventDefault();\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    // Handle any extra behaviour associated with a key event\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.key_event = function(event, name) {\n",
       "\n",
       "    // Prevent repeat events\n",
       "    if (name == 'key_press')\n",
       "    {\n",
       "        if (event.which === this._key)\n",
       "            return;\n",
       "        else\n",
       "            this._key = event.which;\n",
       "    }\n",
       "    if (name == 'key_release')\n",
       "        this._key = null;\n",
       "\n",
       "    var value = '';\n",
       "    if (event.ctrlKey && event.which != 17)\n",
       "        value += \"ctrl+\";\n",
       "    if (event.altKey && event.which != 18)\n",
       "        value += \"alt+\";\n",
       "    if (event.shiftKey && event.which != 16)\n",
       "        value += \"shift+\";\n",
       "\n",
       "    value += 'k';\n",
       "    value += event.which.toString();\n",
       "\n",
       "    this._key_event_extra(event, name);\n",
       "\n",
       "    this.send_message(name, {key: value,\n",
       "                             guiEvent: simpleKeys(event)});\n",
       "    return false;\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
       "    if (name == 'download') {\n",
       "        this.handle_save(this, null);\n",
       "    } else {\n",
       "        this.send_message(\"toolbar_button\", {name: name});\n",
       "    }\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
       "    this.message.textContent = tooltip;\n",
       "};\n",
       "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
       "\n",
       "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
       "\n",
       "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
       "    // Create a \"websocket\"-like object which calls the given IPython comm\n",
       "    // object with the appropriate methods. Currently this is a non binary\n",
       "    // socket, so there is still some room for performance tuning.\n",
       "    var ws = {};\n",
       "\n",
       "    ws.close = function() {\n",
       "        comm.close()\n",
       "    };\n",
       "    ws.send = function(m) {\n",
       "        //console.log('sending', m);\n",
       "        comm.send(m);\n",
       "    };\n",
       "    // Register the callback with on_msg.\n",
       "    comm.on_msg(function(msg) {\n",
       "        //console.log('receiving', msg['content']['data'], msg);\n",
       "        // Pass the mpl event to the overridden (by mpl) onmessage function.\n",
       "        ws.onmessage(msg['content']['data'])\n",
       "    });\n",
       "    return ws;\n",
       "}\n",
       "\n",
       "mpl.mpl_figure_comm = function(comm, msg) {\n",
       "    // This is the function which gets called when the mpl process\n",
       "    // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
       "\n",
       "    var id = msg.content.data.id;\n",
       "    // Get hold of the div created by the display call when the Comm\n",
       "    // socket was opened in Python.\n",
       "    var element = $(\"#\" + id);\n",
       "    var ws_proxy = comm_websocket_adapter(comm)\n",
       "\n",
       "    function ondownload(figure, format) {\n",
       "        window.open(figure.imageObj.src);\n",
       "    }\n",
       "\n",
       "    var fig = new mpl.figure(id, ws_proxy,\n",
       "                           ondownload,\n",
       "                           element.get(0));\n",
       "\n",
       "    // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
       "    // web socket which is closed, not our websocket->open comm proxy.\n",
       "    ws_proxy.onopen();\n",
       "\n",
       "    fig.parent_element = element.get(0);\n",
       "    fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
       "    if (!fig.cell_info) {\n",
       "        console.error(\"Failed to find cell for figure\", id, fig);\n",
       "        return;\n",
       "    }\n",
       "\n",
       "    var output_index = fig.cell_info[2]\n",
       "    var cell = fig.cell_info[0];\n",
       "\n",
       "};\n",
       "\n",
       "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
       "    var width = fig.canvas.width/mpl.ratio\n",
       "    fig.root.unbind('remove')\n",
       "\n",
       "    // Update the output cell to use the data from the current canvas.\n",
       "    fig.push_to_output();\n",
       "    var dataURL = fig.canvas.toDataURL();\n",
       "    // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
       "    // the notebook keyboard shortcuts fail.\n",
       "    IPython.keyboard_manager.enable()\n",
       "    $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
       "    fig.close_ws(fig, msg);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.close_ws = function(fig, msg){\n",
       "    fig.send_message('closing', msg);\n",
       "    // fig.ws.close()\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
       "    // Turn the data on the canvas into data in the output cell.\n",
       "    var width = this.canvas.width/mpl.ratio\n",
       "    var dataURL = this.canvas.toDataURL();\n",
       "    this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.updated_canvas_event = function() {\n",
       "    // Tell IPython that the notebook contents must change.\n",
       "    IPython.notebook.set_dirty(true);\n",
       "    this.send_message(\"ack\", {});\n",
       "    var fig = this;\n",
       "    // Wait a second, then push the new image to the DOM so\n",
       "    // that it is saved nicely (might be nice to debounce this).\n",
       "    setTimeout(function () { fig.push_to_output() }, 1000);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._init_toolbar = function() {\n",
       "    var fig = this;\n",
       "\n",
       "    var nav_element = $('<div/>');\n",
       "    nav_element.attr('style', 'width: 100%');\n",
       "    this.root.append(nav_element);\n",
       "\n",
       "    // Define a callback function for later on.\n",
       "    function toolbar_event(event) {\n",
       "        return fig.toolbar_button_onclick(event['data']);\n",
       "    }\n",
       "    function toolbar_mouse_event(event) {\n",
       "        return fig.toolbar_button_onmouseover(event['data']);\n",
       "    }\n",
       "\n",
       "    for(var toolbar_ind in mpl.toolbar_items){\n",
       "        var name = mpl.toolbar_items[toolbar_ind][0];\n",
       "        var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
       "        var image = mpl.toolbar_items[toolbar_ind][2];\n",
       "        var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
       "\n",
       "        if (!name) { continue; };\n",
       "\n",
       "        var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
       "        button.click(method_name, toolbar_event);\n",
       "        button.mouseover(tooltip, toolbar_mouse_event);\n",
       "        nav_element.append(button);\n",
       "    }\n",
       "\n",
       "    // Add the status bar.\n",
       "    var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
       "    nav_element.append(status_bar);\n",
       "    this.message = status_bar[0];\n",
       "\n",
       "    // Add the close button to the window.\n",
       "    var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
       "    var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
       "    button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
       "    button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
       "    buttongrp.append(button);\n",
       "    var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
       "    titlebar.prepend(buttongrp);\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._root_extra_style = function(el){\n",
       "    var fig = this\n",
       "    el.on(\"remove\", function(){\n",
       "\tfig.close_ws(fig, {});\n",
       "    });\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._canvas_extra_style = function(el){\n",
       "    // this is important to make the div 'focusable\n",
       "    el.attr('tabindex', 0)\n",
       "    // reach out to IPython and tell the keyboard manager to turn it's self\n",
       "    // off when our div gets focus\n",
       "\n",
       "    // location in version 3\n",
       "    if (IPython.notebook.keyboard_manager) {\n",
       "        IPython.notebook.keyboard_manager.register_events(el);\n",
       "    }\n",
       "    else {\n",
       "        // location in version 2\n",
       "        IPython.keyboard_manager.register_events(el);\n",
       "    }\n",
       "\n",
       "}\n",
       "\n",
       "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
       "    var manager = IPython.notebook.keyboard_manager;\n",
       "    if (!manager)\n",
       "        manager = IPython.keyboard_manager;\n",
       "\n",
       "    // Check for shift+enter\n",
       "    if (event.shiftKey && event.which == 13) {\n",
       "        this.canvas_div.blur();\n",
       "        // select the cell after this one\n",
       "        var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
       "        IPython.notebook.select(index + 1);\n",
       "    }\n",
       "}\n",
       "\n",
       "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
       "    fig.ondownload(fig, null);\n",
       "}\n",
       "\n",
       "\n",
       "mpl.find_output_cell = function(html_output) {\n",
       "    // Return the cell and output element which can be found *uniquely* in the notebook.\n",
       "    // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
       "    // IPython event is triggered only after the cells have been serialised, which for\n",
       "    // our purposes (turning an active figure into a static one), is too late.\n",
       "    var cells = IPython.notebook.get_cells();\n",
       "    var ncells = cells.length;\n",
       "    for (var i=0; i<ncells; i++) {\n",
       "        var cell = cells[i];\n",
       "        if (cell.cell_type === 'code'){\n",
       "            for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
       "                var data = cell.output_area.outputs[j];\n",
       "                if (data.data) {\n",
       "                    // IPython >= 3 moved mimebundle to data attribute of output\n",
       "                    data = data.data;\n",
       "                }\n",
       "                if (data['text/html'] == html_output) {\n",
       "                    return [cell, data, j];\n",
       "                }\n",
       "            }\n",
       "        }\n",
       "    }\n",
       "}\n",
       "\n",
       "// Register the function which deals with the matplotlib target/channel.\n",
       "// The kernel may be null if the page has been refreshed.\n",
       "if (IPython.notebook.kernel != null) {\n",
       "    IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
       "}\n"
      ],
      "text/plain": [
       "<IPython.core.display.Javascript object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<img src=\"\" width=\"432\">"
      ],
      "text/plain": [
       "<IPython.core.display.HTML object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "result, history = backtest(MAMAStrategy,df, verbose=False, return_history=True)\n",
    "# result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strat_id</th>\n",
       "      <th>init_cash</th>\n",
       "      <th>buy_prop</th>\n",
       "      <th>sell_prop</th>\n",
       "      <th>commission</th>\n",
       "      <th>stop_loss</th>\n",
       "      <th>stop_trail</th>\n",
       "      <th>execution_type</th>\n",
       "      <th>channel</th>\n",
       "      <th>symbol</th>\n",
       "      <th>...</th>\n",
       "      <th>rnorm100</th>\n",
       "      <th>len</th>\n",
       "      <th>drawdown</th>\n",
       "      <th>moneydown</th>\n",
       "      <th>max</th>\n",
       "      <th>maxdrawdown</th>\n",
       "      <th>maxdrawdownperiod</th>\n",
       "      <th>sharperatio</th>\n",
       "      <th>pnl</th>\n",
       "      <th>final_value</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100000</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.0075</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>close</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>...</td>\n",
       "      <td>-15.273058</td>\n",
       "      <td>49</td>\n",
       "      <td>12.742883</td>\n",
       "      <td>13638.337586</td>\n",
       "      <td>{'len': 49, 'drawdown': 12.742882982202364, 'm...</td>\n",
       "      <td>12.742883</td>\n",
       "      <td>49</td>\n",
       "      <td>None</td>\n",
       "      <td>-6611.24</td>\n",
       "      <td>93388.758285</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>1 rows × 31 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   strat_id  init_cash  buy_prop  sell_prop  commission stop_loss stop_trail  \\\n",
       "0         0     100000         1          1      0.0075      None       None   \n",
       "\n",
       "  execution_type channel symbol  ...   rnorm100  len   drawdown     moneydown  \\\n",
       "0          close    None   None  ... -15.273058   49  12.742883  13638.337586   \n",
       "\n",
       "                                                 max  maxdrawdown  \\\n",
       "0  {'len': 49, 'drawdown': 12.742882982202364, 'm...    12.742883   \n",
       "\n",
       "   maxdrawdownperiod  sharperatio      pnl   final_value  \n",
       "0                 49         None -6611.24  93388.758285  \n",
       "\n",
       "[1 rows x 31 columns]"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strat_id</th>\n",
       "      <th>strat_name</th>\n",
       "      <th>dt</th>\n",
       "      <th>type</th>\n",
       "      <th>price</th>\n",
       "      <th>size</th>\n",
       "      <th>value</th>\n",
       "      <th>commission</th>\n",
       "      <th>pnl</th>\n",
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       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-03-13</td>\n",
       "      <td>buy</td>\n",
       "      <td>45.227501</td>\n",
       "      <td>2192</td>\n",
       "      <td>99138.682007</td>\n",
       "      <td>743.540115</td>\n",
       "      <td>0.000000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-03-26</td>\n",
       "      <td>sell</td>\n",
       "      <td>46.697498</td>\n",
       "      <td>-2192</td>\n",
       "      <td>99138.682007</td>\n",
       "      <td>767.706872</td>\n",
       "      <td>3222.234314</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-04-04</td>\n",
       "      <td>buy</td>\n",
       "      <td>48.837502</td>\n",
       "      <td>2065</td>\n",
       "      <td>100849.440651</td>\n",
       "      <td>756.370805</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-04-12</td>\n",
       "      <td>sell</td>\n",
       "      <td>49.717499</td>\n",
       "      <td>-2065</td>\n",
       "      <td>100849.440651</td>\n",
       "      <td>769.999762</td>\n",
       "      <td>1817.194328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-04-24</td>\n",
       "      <td>buy</td>\n",
       "      <td>51.869999</td>\n",
       "      <td>1949</td>\n",
       "      <td>101094.627918</td>\n",
       "      <td>758.209709</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-04-26</td>\n",
       "      <td>sell</td>\n",
       "      <td>51.075001</td>\n",
       "      <td>-1949</td>\n",
       "      <td>101094.627918</td>\n",
       "      <td>746.588824</td>\n",
       "      <td>-1549.451431</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-06</td>\n",
       "      <td>buy</td>\n",
       "      <td>52.937500</td>\n",
       "      <td>1853</td>\n",
       "      <td>98093.187500</td>\n",
       "      <td>735.698906</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-07</td>\n",
       "      <td>sell</td>\n",
       "      <td>50.715000</td>\n",
       "      <td>-1853</td>\n",
       "      <td>98093.187500</td>\n",
       "      <td>704.811715</td>\n",
       "      <td>-4118.292217</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   strat_id strat_name         dt  type      price  size          value  \\\n",
       "0         0            2019-03-13   buy  45.227501  2192   99138.682007   \n",
       "1         0            2019-03-26  sell  46.697498 -2192   99138.682007   \n",
       "2         0            2019-04-04   buy  48.837502  2065  100849.440651   \n",
       "3         0            2019-04-12  sell  49.717499 -2065  100849.440651   \n",
       "4         0            2019-04-24   buy  51.869999  1949  101094.627918   \n",
       "5         0            2019-04-26  sell  51.075001 -1949  101094.627918   \n",
       "6         0            2019-05-06   buy  52.937500  1853   98093.187500   \n",
       "7         0            2019-05-07  sell  50.715000 -1853   98093.187500   \n",
       "\n",
       "   commission          pnl  \n",
       "0  743.540115     0.000000  \n",
       "1  767.706872  3222.234314  \n",
       "2  756.370805     0.000000  \n",
       "3  769.999762  1817.194328  \n",
       "4  758.209709     0.000000  \n",
       "5  746.588824 -1549.451431  \n",
       "6  735.698906     0.000000  \n",
       "7  704.811715 -4118.292217  "
      ]
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     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "history['orders']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>strat_id</th>\n",
       "      <th>strat_name</th>\n",
       "      <th>dt</th>\n",
       "      <th>MACD (12, 16, 9)</th>\n",
       "      <th>CrossOver</th>\n",
       "      <th>ALMA</th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>39.309765</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-01-03</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>38.916997</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td></td>\n",
       "      <td>2019-01-04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>38.217196</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-01-07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.482952</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-01-08</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>37.115681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
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       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-24</td>\n",
       "      <td>-0.530910</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.578881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>100</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-28</td>\n",
       "      <td>-0.553283</td>\n",
       "      <td>0.0</td>\n",
       "      <td>45.182577</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>101</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-29</td>\n",
       "      <td>-0.567664</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.788032</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-30</td>\n",
       "      <td>-0.559801</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.557318</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>103</th>\n",
       "      <td>0</td>\n",
       "      <td></td>\n",
       "      <td>2019-05-31</td>\n",
       "      <td>-0.573030</td>\n",
       "      <td>0.0</td>\n",
       "      <td>44.396019</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>104 rows × 6 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     strat_id strat_name         dt  MACD (12, 16, 9)  CrossOver       ALMA\n",
       "0           0            2019-01-02               NaN        NaN  39.309765\n",
       "1           0            2019-01-03               NaN        NaN  38.916997\n",
       "2           0            2019-01-04               NaN        NaN  38.217196\n",
       "3           0            2019-01-07               NaN        NaN  37.482952\n",
       "4           0            2019-01-08               NaN        NaN  37.115681\n",
       "..        ...        ...        ...               ...        ...        ...\n",
       "99          0            2019-05-24         -0.530910        0.0  45.578881\n",
       "100         0            2019-05-28         -0.553283        0.0  45.182577\n",
       "101         0            2019-05-29         -0.567664        0.0  44.788032\n",
       "102         0            2019-05-30         -0.559801        0.0  44.557318\n",
       "103         0            2019-05-31         -0.573030        0.0  44.396019\n",
       "\n",
       "[104 rows x 6 columns]"
      ]
     },
     "execution_count": 12,
     "metadata": {},
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    }
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
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